Coherence evaluation is a problem related to the area of natural language processing whose complexity lies mainly in the analysis of the semantics and context of the words in the text. Fortunately, the Bidirectional Encoder Representation from Transformers (BERT) architecture can capture the aforementioned variables and represent them as embeddings to perform Fine-tunings. The present study proposes a Second Fine-Tuned model based on BERT to detect inconsistent sentences (coherence evaluation) in scientific abstracts written in English/Spanish. For this purpose, 2 formal methods for the generation of inconsistent abstracts have been proposed: Random Manipulation (RM) and K-means Random Manipulation (KRM). Six experiments were performed; showing that performing Second Fine-Tuned improves the detection of inconsistent sentences with an accuracy of 71%. This happens even if the new retraining data are of different language or different domain. It was also shown that using several methods for generating inconsistent abstracts and mixing them when performing Second Fine-Tuned does not provide better results than using a single technique.
|Number of pages||9|
|Journal||International Journal of Advanced Computer Science and Applications|
|State||Published - 2022|
Bibliographical noteFunding Information:
To the Universidad Nacional de San Agustín de Arequipa for the funding granted to the project ”Transmedia, Gam-ification and Video games to promote scientific writing in Engineering students”, under Contract No. IBA-IB-38-2020-UNSA. We would like to thank to the ”Research Center, Transfer of Technologies and Software Development R+D+i” – CiTeSoft-EC-0003-2017-UNSA, for their collaboration in the use of their equipment and facilities, for the development of this research work.
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- Coherence evaluation
- Inconsistent sentences detection
- Second fine-tuned